The present invention relates to vision systems. More in particular it relates to camera scheduling in vision systems.
Vision systems are increasingly being deployed to perform many complex tasks such as gait or face recognition. While improved algorithms are being developed to perform these tasks, it is also imperative that data suitable for these algorithms is acquired—a nontrivial task in a dynamic and crowded scene. A multi-camera system that collects images and videos of moving objects in such scenes, is subject to task constraints. The system constructs “task visibility intervals” that contain information about what can be sensed in certain future time intervals. Constructing these intervals requires prediction of future object motion and consideration of several factors such as object occlusion and camera control parameters. Although cameras can be scheduled based on the constructed intervals, looking for an optimal schedule is a typical NP-complete problem. There is also a lack of exact future information in a dynamic environment.
Accordingly novel methods for fast camera scheduling that take particular constraints into consideration and yield solutions that can be proven to be within a certain factor of an optimal solution are required.